Predict profitability of trades based on indicator buy / sell signals

Overview

Predict profitability of trades based on indicator buy / sell signals

Trade profitability analysis for trades based on various indicators signals:

  • MACD
  • Simple Moving Average
  • Exponential Moving Average

  • Trading assumptions:
    1. Trade is profitable if, profit >0
    2. Buy / sell happen the following day of the signal
    3. Buy / sell are taken 10% from the open price towards close price

    Machine learning assumptions:
    • Binary classification: 1 - profit, 0 - loss
    • A separate model for each company / ticker
    • Model is trained vs optimal precision

    Machine learning models used:
    1. Linear Support Vector Classifier
    2. Decision Tree Classifier
    3. Random Forest Classifier
    4. Gradient Boosting Classifier
    5. XGBoost Classifier
    6. Keras classifier

    Trade analysis intermediate results:
    30-40% of trades based on indicator signals are profitable
    In general trades on SMA signals are more often profitable than the ones based on EMA signals

    Trade profitability predictions intermediate results (based on test data)/
    The precision of the predictions is oscilating around 70%, which is pretty good, considering that the analysts estimate other signals accuracy as 30 to 50% (double top, shoulder & arms, etc). This means, there is ~70% chance that predicted trade will be profitable (Reminder: profitable -> profit > 0)
    However, the recall is only around 15%, which means that very the model pick-up very few of the actually profitable trades.

    #Detailed analysis tbc

    Owner
    Tomasz Porzycki
    Tomasz Porzycki
    A Time Series Library for Apache Spark

    Flint: A Time Series Library for Apache Spark The ability to analyze time series data at scale is critical for the success of finance and IoT applicat

    Two Sigma 970 Jan 04, 2023
    Kaggler is a Python package for lightweight online machine learning algorithms and utility functions for ETL and data analysis.

    Kaggler is a Python package for lightweight online machine learning algorithms and utility functions for ETL and data analysis. It is distributed under the MIT License.

    Jeong-Yoon Lee 720 Dec 25, 2022
    InfiniteBoost: building infinite ensembles with gradient descent

    InfiniteBoost Code for a paper InfiniteBoost: building infinite ensembles with gradient descent (arXiv:1706.01109). A. Rogozhnikov, T. Likhomanenko De

    Alex Rogozhnikov 183 Jan 03, 2023
    Primitives for machine learning and data science.

    An Open Source Project from the Data to AI Lab, at MIT MLPrimitives Pipelines and primitives for machine learning and data science. Documentation: htt

    MLBazaar 65 Dec 29, 2022
    SageMaker Python SDK is an open source library for training and deploying machine learning models on Amazon SageMaker.

    SageMaker Python SDK SageMaker Python SDK is an open source library for training and deploying machine learning models on Amazon SageMaker. With the S

    Amazon Web Services 1.8k Jan 01, 2023
    This repository contains the code to predict house price using Linear Regression Method

    House-Price-Prediction-Using-Linear-Regression The dataset I used for this personal project is from Kaggle uploaded by aariyan panchal. Link of Datase

    0 Jan 28, 2022
    Mesh TensorFlow: Model Parallelism Made Easier

    Mesh TensorFlow - Model Parallelism Made Easier Introduction Mesh TensorFlow (mtf) is a language for distributed deep learning, capable of specifying

    1.3k Dec 26, 2022
    A machine learning web application for binary classification using streamlit

    Machine Learning web App This is a machine learning web application for binary classification using streamlit options this application contains 3 clas

    abdelhak mokri 1 Dec 20, 2021
    Simple, fast, and parallelized symbolic regression in Python/Julia via regularized evolution and simulated annealing

    Parallelized symbolic regression built on Julia, and interfaced by Python. Uses regularized evolution, simulated annealing, and gradient-free optimization.

    Miles Cranmer 924 Jan 03, 2023
    Implementation of deep learning models for time series in PyTorch.

    List of Implementations: Currently, the reimplementation of the DeepAR paper(DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks

    Yunkai Zhang 275 Dec 28, 2022
    Dual Adaptive Sampling for Machine Learning Interatomic potential.

    DAS Dual Adaptive Sampling for Machine Learning Interatomic potential. How to cite If you use this code in your research, please cite this using: Hong

    6 Jul 06, 2022
    This project has Classification and Clustering done Via kNN and K-Means respectfully

    This project has Classification and Clustering done Via kNN and K-Means respectfully. It later tests its efficiency via F1/accuracy/recall/precision for kNN and Davies-Bouldin Index for Clustering. T

    Mohammad Ali Mustafa 0 Jan 20, 2022
    A statistical library designed to fill the void in Python's time series analysis capabilities, including the equivalent of R's auto.arima function.

    pmdarima Pmdarima (originally pyramid-arima, for the anagram of 'py' + 'arima') is a statistical library designed to fill the void in Python's time se

    alkaline-ml 1.3k Dec 22, 2022
    This is the material used in my free Persian course: Machine Learning with Python

    This is the material used in my free Persian course: Machine Learning with Python

    Yara Mohamadi 4 Aug 07, 2022
    Apple-voice-recognition - Machine Learning

    Apple-voice-recognition Machine Learning How does Siri work? Siri is based on large-scale Machine Learning systems that employ many aspects of data sc

    Harshith VH 1 Oct 22, 2021
    A Python implementation of GRAIL, a generic framework to learn compact time series representations.

    GRAIL A Python implementation of GRAIL, a generic framework to learn compact time series representations. Requirements Python 3.6+ numpy scipy tslearn

    3 Nov 24, 2021
    A simple machine learning python sign language detection project.

    SST Coursework 2022 About the app A python application that utilises the tensorflow object detection algorithm to achieve automatic detection of ameri

    Xavier Koh 2 Jun 30, 2022
    Traingenerator 🧙 A web app to generate template code for machine learning ✨

    Traingenerator 🧙 A web app to generate template code for machine learning ✨ 🎉 Traingenerator is now live! 🎉

    Johannes Rieke 1.2k Jan 07, 2023
    LightGBM + Optuna: no brainer

    AutoLGBM LightGBM + Optuna: no brainer auto train lightgbm directly from CSV files auto tune lightgbm using optuna auto serve best lightgbm model usin

    Rishiraj Acharya 22 Dec 15, 2022
    Distributed Tensorflow, Keras and PyTorch on Apache Spark/Flink & Ray

    A unified Data Analytics and AI platform for distributed TensorFlow, Keras and PyTorch on Apache Spark/Flink & Ray What is Analytics Zoo? Analytics Zo

    2.5k Dec 28, 2022